Chromatin organization drives the search mechanism of nuclear factors

Nuclear factors rapidly scan the genome for their targets, but the role of nuclear organization in such search is uncharted. Here we analyzed how multiple factors explore chromatin, combining live-cell single-molecule tracking with multifocal structured illumination of DNA density. We find that factors displaying higher bound fractions sample DNA-dense regions more exhaustively. Focusing on the tumor-suppressor p53, we demonstrate that it searches for targets by alternating between rapid diffusion in the interchromatin compartment and compact sampling of chromatin dense regions. Efficient targeting requires balanced interactions with chromatin: fusing p53 with an exogenous intrinsically disordered region potentiates p53-mediated target gene activation at low concentrations, but leads to condensates at higher levels, derailing its search and downregulating transcription. Our findings highlight the role of disordered regions on factors search and showcase a powerful method to generate traffic maps of the eukaryotic nucleus to dissect how its organization guides nuclear factors action.

We evaluated the correlation between NFs molecular weight and enrichment in chromatin density, accounting for the expected oligomerization when they diffuse in the nucleus.Here HaloTag, H2B-HaloTag and CTCF-HaloTag are considered monomers, p65-Halotag is considered in complex with its partner p50 (untagged), p53-NT is considered dimeric (both tagged) and p53-IRR is considered tetrameric (all subunits tagged).No significant correlation is found between the two variables.Source data: same as fig. 1. (c) We use vbSPT to classify track segments into bound and diffusing components, and then filtered out the segments identified as bound molecules, in order to focus the analysis of diffusional anisotropy on the diffusing components only.To check that vbSPT successfully identified bound segments, we reanalyzed the distribution of displacements for each of the factors, after discarding those bound segments.In every case the residual bound fraction was estimated to be less than 5%.Source data (starting from data of fig.2) are provided as Source Data file.Identification of the lattice vectors.To identify the lattice vector we first localize the positions at which illumination spots appear in the sample by using the ThunderStorm plug-in 2 in ImageJ/FIJI.These coordinates are then used to generate a stack of binary images with ones at the pixels where the illumination spots have been localized and zero elsewhere.The images are then Fourier transformed and the Fourier magnitude images are then averaged together, giving rise to a periodic lattice of peaks, spaced by the inverse of the average spacing between peaks in the real images.Next, we search for peaks in the Fourier dimension, verify that we can find harmonics of the peaks found at lowest spatial frequency, and identify three candidate peaks with the lowest spatial frequency (that shows harmonics).We next verify that the vector sum and differences of the position vector of these identified peaks, also point to a detectable peak.If two vectors satisfy these conditions they are chosen as the lattice vectors in the Fourier space, and are then Fourier transformed to obtain real lattice vectors.

Supplementary
Identification of the offset vectors.Once we have the lattice vectors we can find the position of any other illumination spot in one of the images of the raw stack by knowing the position of one of them (i.e. by finding the position of the top left illumination spot in each them image, the offset vectors).To this scope we Supplemental Note 2: Correction of the distribution of displacements model for molecules going out of focus.
In SMT only molecules positioned within a slice of thickness of approximately 1μm around the focal plane can be localized and tracked.As a consequence, molecules diffusing with faster diffusion coefficients are more likely to diffuse out of focus, resulting in an underestimation of the fraction of molecules involved in this fast diffusion.
As described in the Methods section of the main text, we fit the distribution of displacements with a multicomponent diffusion model, described by: (Eq S.1) Here Δ , , accounts for the probability of molecules of still staying in the slice with limits −Δ to +Δ around the focal plane.Considering that within this slice a single molecule can have any starting position , Δ , can be calculated as: , we obtain: Where we used the symmetric function erf θ = 2/! 8 # $1 ' d.
1 9 To solve this, we split the integral in two: In the first integral < we can substitute: .= to yield: Similar, in the second integral, we substitute:.
Summing up the terms for < and <<, we obtain: Which is the final expression for Δ , used in Eq.S1.

Supplementary Figure 2 .
Additional controls on NFs dynamics.(a) We quantified expression levels by western-blots for the two the two ectopically expressed factors, H2B-HaloTag (top) expression levels are very low compared to the endogenous H2B level (the H2B and the H2B-HaloTag signals shown on the western blot panel are obtained with different exposition times, 3.4 sec and 9.1 sec respectively.The arrow indicates the H2B-HaloTag band).p65-HaloTag is on average 1,4 fold more expressed than the endogenous p65 (nreplicates = 3, statistical test Student's t-test, error bars: SD).(b)

Figure 3 .
Characterization of the p53-HaloTag knock-in (KI) cell line.(a) HaloTag-p53 displays the expected nuclear localization and accumulation in response to activation by 10-Gy IR.(b) Western-blot analysis reveals that the p53-HaloTag KI cell line displays accumulation of p53 and its target p21 upon activation by 10-Gy IR or 4OHT, while the p53-knock out (KO) cell line does not.(c) Analysis of p53 target gene expression by RT-qPCR displayed similar induction of p53 target genes in parental cells and p53-Halotag KI cells and no induction in p53-KO cells (error bars: SD, nreplicates = 3, statistical test: Student's t-test).(d) Time-course of target gene expression for CDKN1A and MDM2 in p53-Halotag KI highlights a peak in p53 transcriptional activity between 4 and 5 hours post irradiation.(e) Model selection on diffusion coefficients extracted by mean-squared displacements analysis highlight that a model with three components (one bound + two diffusing) describes p53 mobility both in untreated conditions and upon 10-Gy IR.(f) vbSPT can be used to correctly filter out the bound population of p53 molecules.(g) Co-clustering of p53 bound and diffusing molecules by cross-correlation analysis.Slow diffusing p53 molecules co-cluster more frequently with bound molecules than fast ones.Source data (starting from data of fig. 3) are provided as Source Data file.Supplementary Figure 4. Analysis of p53-HaloTag diffusion upon induction of DNA damage by AsiSI activation in DIvA p53-HaloTag knock-in cells.DIvA cells were treated with 4-OHT for 4 hrs to induce AsiSI translocation in the cell nucleus and analyzed with our SMT/mSIM pipeline.Induction of DNA damage by AsiSI results in increased p53 bound fraction (a) (ncells = 29, 29 for untreated and 4-OHT respectively, statistical test Kolmogorov-Smirnov) and a diffusional anisotropy profile compatible with guided exploration (b) (error bars: s.e.m., evaluated by bootstrapping).Slow diffusing/bound p53 molecules localize in regions at higher DNA density than fast diffusing molecules, as evidenced by plotting p53 localization frequency in chromatin depending on their speed (c) and the radial Hoechst profile around p53 molecules (d).Source data are provided as Source Data file.Supplementary Note 1: Reconstruction of mSIM imagesAs described by York et al. 1 in order to reconstruct a super-resolved and optically sectioned image from the 224 individual frames acquired in our mSIM set-up, it is necessary to: (i) identify the position of the illumination spots in the image plane at each acquisition frame; (ii) perform digital pinholing of the individual frames to get rid of out-of-focus blur; (iii) fuse together the images through pixel-reassignment that result in a √2 increase in the lateral resolution of the microscope.All these steps are performed with custom-written routines in Matlab.Identification of the illumination spot position: The position at which illumination spots appears in the image, does not necessarily correspond to the points in which the DMD spots illuminate the sample, but rather they are the convolution product of the illumination pattern projected on the sample for the actual distribution of fluorescent labels in the sample.On average, however, the distance between the recorded spots should reflect the average distance between illumination points.Similar to what was performed in York et al. 1 , we use this principle to find the lattice vectors that describe the 2D displacement between any two illumination spots.The position of all illumination points in the acquisition stack can be defined by two set of vectors (that need to be found for each individual acquisition): (i) the lattice vectors that describe the displacement between an illumination spot and the two nearest neighbor ones; (ii) the offset vectors, that specify the absolute position of the illumination spot closer to the top-left corner of the image in each of the images of the raw acquisition.